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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with |
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the License. You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on |
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the |
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specific language governing permissions and limitations under the License. |
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# Overview |
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A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like [`StableDiffusionPipeline`] or [`StableDiffusionControlNetPipeline`], with specific capabilities. All pipeline types inherit from the base [`DiffusionPipeline`] class; pass it any checkpoint, and it'll automatically detect the pipeline type and load the necessary components. |
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This section introduces you to some of the tasks supported by our pipelines such as unconditional image generation and different techniques and variations of text-to-image generation. You'll also learn how to gain more control over the generation process by setting a seed for reproducibility and weighting prompts to adjust the influence certain words in the prompt has over the output. Finally, you'll see how you can create a community pipeline for a custom task like generating images from speech. |
